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"https://api.github.com/repos/pandas-dev/pandas/issues/1" | "https://api.github.com/repos/pandas-dev/pandas" | "https://api.github.com/repos/pandas-dev/pandas/issues/1/labels{/name}" | "https://api.github.com/repos/pandas-dev/pandas/issues/1/comments" | "https://api.github.com/repos/pandas-dev/pandas/issues/1/events" | "https://github.com/pandas-dev/pandas/issues/1" | 337,721 | "MDU6SXNzdWUzMzc3MjE=" | 1 | "Enable element-wise comparison operations in DataMatrix objects" | {
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} | [] | "closed" | false | null | [] | null | 1 | "2010-09-29T00:45:31" | "2014-02-11T20:40:01" | "2011-02-19T23:13:48" | "MEMBER" | null | "re: pystatsmodels e-mail
hi everyone,
just getting started with pandas and i was wondering if someone could
help me out. do pandas.DataMatrix objects support per item comparison
operations?
i have a two data matrices, and i want to do something like this:
div[div > 0.5 \* price] = 0
this would work if div and price were numpy.ndarray objects. any idea
how i would do something like this with pandas.DataMatrix objects?
thanks,
andy
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"https://api.github.com/repos/pandas-dev/pandas/issues/3" | "https://api.github.com/repos/pandas-dev/pandas" | "https://api.github.com/repos/pandas-dev/pandas/issues/3/labels{/name}" | "https://api.github.com/repos/pandas-dev/pandas/issues/3/comments" | "https://api.github.com/repos/pandas-dev/pandas/issues/3/events" | "https://github.com/pandas-dev/pandas/issues/3" | 337,728 | "MDU6SXNzdWUzMzc3Mjg=" | 3 | "Binary operations on int DataMatrix" | {
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} | [] | "closed" | false | null | [] | null | 1 | "2010-09-29T00:50:52" | "2014-09-15T17:20:09" | "2011-01-01T23:50:12" | "MEMBER" | null | "Reported by kwgoodman, Jun 12, 2010
When adding two int DataMatrix's a ValueError is raised when matrix.py attempt to fill missing values with NaN:
> > dma1 = pandas.DataMatrix([[1, 2], [3, 4]], ['a', 'b'], ['c', 'd'])
> > dma2 = pandas.DataMatrix([[1, 2], [3, 4]], ['b', 'a'], ['d', 'c'])
> > dma1 + dma2
> > <snip>
> > ValueError: cannot convert float NaN to integer
Delete comment Comment 1 by kwgoodman, Jun 12, 2010
Possible fix:
> > x = np.array([1.0])
> > issubclass(x.dtype.type, np.inexact)
> > True
> > x = np.array([1])
> > issubclass(x.dtype.type, np.inexact)
> > False
> > x = np.array([1.0], dtype=object)
> > issubclass(x.dtype.type, np.inexact)
> > False
> > x = np.array([1.0], dtype=str)
> > issubclass(x.dtype.type, np.inexact)
> > False
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} | [] | "closed" | false | null | [] | null | 1 | "2010-09-29T00:51:27" | "2014-02-11T20:40:01" | "2010-12-11T06:14:32" | "MEMBER" | null | "Reported by twhitcomb, Jul 27, 2010
What steps will reproduce the problem?
1. Import pandas
2. Create a sample DataFrame
3. Plot the DataFrame using a linewidth keyword argument.
What is the expected output? What do you see instead?
I expect to see the linewidth keyword argument passed through to the plotting routine. Instead, the plot is displayed with the default linewidth (see attached figure).
What version of the product are you using? On what operating system?
> > > pandas.**version**
> > > 0.20000000000000001
> > > Microsoft Windows Vista, Python(x,y) 2.6.5.1
Please provide any additional information below.
Looking at the plot function in frame.py it's obvious why this is happening:
def plot(self, kind='line', **kwds): # pragma: no cover
from pylab import plot
```
for col in sorted(self.columns):
s = self[col]
plot(s.index, s, label=col)
```
Note that **kwds is not used in the plot command.
If I load a new function into my workspace like
def plot_frame(frame, *_kwargs):
from pylab import plot
for col in sorted(frame.columns):
s = frame[col]
plot(s.index, s, label=col, *_kwargs)
Then the arguments are correctly passed on, and I get the proper response, as shown in the other attached figure.
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} | [] | "closed" | false | null | [] | null | 1 | "2010-09-29T05:30:56" | "2014-02-11T20:40:01" | "2010-12-11T06:05:26" | "MEMBER" | null | "Should have min_periods arguments like the other rolling moment functions
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>>> a = DataMatrix([[1.0,2.0,3.0],[4.0,5.0,6.0]], range(2), range(3))
>>> b = DataMatrix(a, dtype=int)
>>> b.values.dtype
dtype('float64')
```
Not sure if this was a design decision or not, but it seems like the dtype of b.values should be int.
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] | "closed" | false | null | [] | null | 3 | "2010-09-29T19:45:47" | "2014-02-11T20:40:01" | "2011-06-23T04:50:05" | "NONE" | null | "```
In [28]: np.fix(Series([1,2,3], range(3)))
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
C:\alei1\basic_mktneutral\<ipython console> in <module>()
C:\Python26\lib\site-packages\numpy\lib\ufunclike.pyc in fix(x, y)
43 x = nx.asanyarray(x)
44 if y is None:
---> 45 y = nx.zeros_like(x)
46 y1 = nx.floor(x)
47 y2 = nx.ceil(x)
C:\Python26\lib\site-packages\numpy\core\numeric.pyc in zeros_like(a)
92 if isinstance(a, ndarray):
93 res = ndarray.__new__(type(a), a.shape, a.dtype, order=a.flags.fnc)
---> 94 res.fill(0)
95 return res
96 try:
C:\Python26\lib\site-packages\pandas\core\series.pyc in fill(self, value, method)
822 """
823 if value is not None:
--> 824 newSeries = self.copy()
825 newSeries[isnull(newSeries)] = value
826 return newSeries
C:\Python26\lib\site-packages\pandas\core\series.pyc in copy(self)
342
343 def copy(self):
--> 344 return Series(self.values.copy(), index=self.index)
345
346 #-------------------------------------------------------------------------------
C:\Python26\lib\site-packages\pandas\core\series.pyc in __new__(cls, data, index, dtype, copy)
136
137 if index is None:
--> 138 raise Exception('Index cannot be None!')
139
140 # This is to prevent mixed-type Series getting all casted to
```
i think the bigger problem is overriding the default behavior of the fill() method, which may contribute to other numpy / scipy functions not behaving correctly.
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} | [] | "closed" | false | null | [] | null | 1 | "2010-09-30T22:33:14" | "2014-07-01T02:55:23" | "2011-06-23T19:38:51" | "MEMBER" | null | "Need to incorporate the selection of ranges of data and write unit tests
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} | [] | "closed" | false | null | [] | null | 2 | "2010-10-03T17:20:41" | "2010-12-11T22:53:14" | "2010-12-11T22:53:14" | "NONE" | null | "First, thank you for the pandas package -- it's incredibly useful and well done.
I know that one of the fundamental concepts behind the data structures is that column ordering doesn't matter. And, as long as one only uses pandas' data access/manipulation functions (eg, sum(), ewma(), etc.), this works fine. But often, it's useful to access the underling values in a numpy array for some more complicated data manipulation. Using the values attribute (or values() method for a series) does this, but it's not always obvious what order the values come back in.
For example:
<pre>
In [1]: dm = DataMatrix(np.arange(2*3).reshape(2,3), index=[1,0], columns=['B', 'A', 'C' ])
In [2]: dm
Out[2]:
B A C
1 0 1 2
0 3 4 5
In [3]: df = DataFrame(np.arange(2*3).reshape(2,3), index=[1,0], columns=['B', 'A', 'C' ])
In [4]: df
Out[4]:
A B C
1 1 0 2
0 4 3 5
In [5]: df.values
Out[5]:
array([[1, 0, 2],
[4, 3, 5]])
In [6]: dm.values
Out[6]:
array([[0, 1, 2],
[3, 4, 5]])
</pre>
DataMatrix seems to respect the passed in ordering of columns, while DataFrame does not. I know this is documented, and not the biggest deal in the world, but does seem to cause quite a bit of confusion for some. Is it possible to have both data types keep the ordering that's passed in? If a user passes in the same column name twice, could this just throw an exception? Something stills need to be done when an operation is performed on two DataFrames (eg, combining them), but instead of reordering in alphabetical order, how about preserving the column ordering from left to right?
Anyway, my bigger concern is actually the following:
<pre>
In [7]: dm.reindex(columns=['C','B','A']).values
Out[7]:
array([[2, 0, 1],
[5, 3, 4]])
In [8]: df.reindex(columns=['C','B','A']).values
Out[8]:
array([[1, 0, 2],
[4, 3, 5]])
</pre>
Regardless of the ordering of the columns after creating a DataFrame/Matrix, a naive users (ie, me) would expect calling reindex and values would return an ndarray with the columns in the same order as was requested. But it looks like this only happens for DataMatrixes (and I'm not even sure that's always guaranteed).
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} | 4 | "2010-10-07T23:42:34" | "2012-12-13T01:37:53" | "2012-12-12T21:35:57" | "NONE" | null | "Hello,
I not sure if this was the best way to suggest improvements, but here I go anyway :)
I really like the easy of the HDFStore (and the entire project for that matter), but I wanted the ability to store and retrieve DataFrames in Groups below the root. .i.e
```
h5 = HDFStore('test.h5')
h5['/groups/below/theroot'] = DataFrame(data, index)
df = h5['/groups/below/theroot']
```
I made the following changes to HDFStore class to do this, and am now using it in production code. If you agree this is something useful then I would like this to become apart of the main code base, using your own approach or the below.
I changed the **repr**, **getitem**, and _write_group functions in pandas.io.pytables.
```
def __repr__(self):
output = str(self.__class__) + '\n'
#Exstract path and kind of all 'pandas_type' pytable Groups.
keys, values = zip(*((x._v_pathname, x._v_attrs.pandas_type) for x in self.handle.walkGroups() if hasattr(x._v_attrs,'pandas_type')))
output += adjoin(5, keys, values)
return output
def __getitem__(self, key):
if not key[0] == '/': #Then add root slash so we can use getNode belwo
key = '/' + key
group = self.handle.getNode(key)
return _read_group(group)
def _write_group(self, key, value):
root = self.handle.root
if key[0] == '/': #Assume they want a nested pytable Group
final_slash = key.rfind('/')
where = key[:final_slash]
name = key[final_slash + 1:]
else:
where = '/'
name = key
try:
group = self.handle.getNode(key)
except:
group = self.handle.createGroup(where, name, createparents=True)
kind = type(value)
handler = self._get_write_handler(kind)
try:
handler(group, value)
except Exception:
raise
group._v_attrs.pandas_type = kind.__name__
return True
```
please let me know if there is questions or concerns... I made it so that it still works just like the original if the user doesn't need groups...
Thanks for a great project.
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} | [] | "closed" | false | null | [] | null | 1 | "2010-10-11T03:19:39" | "2011-02-19T16:13:00" | "2011-02-19T16:13:00" | "MEMBER" | null | "For example:
```
df.add(series, axis=0)
df.add(series, axis='index')
df.add(series, axis='columns')
```
Etc., might be useful in certain cases
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} | [] | "closed" | false | null | [] | null | 1 | "2010-10-12T16:10:48" | "2010-12-11T06:19:58" | "2010-12-11T06:19:58" | "MEMBER" | null | "DataFrame.apply, Series.applymap, Series.map sort of come across as inconsistent.
```
def f(x):
return x.apply(lambda y: 2*y)
def g(x):
return x.applymap(lambda y: 2*y)
a = TimeSeries([1,2],[1,2])
b = DataMatrix({'a':a})
f(b)
g(b)
f(a)
g(a)
```
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} | [] | "closed" | false | null | [] | null | 1 | "2010-10-22T17:59:31" | "2011-01-11T03:37:14" | "2011-01-11T03:37:14" | "MEMBER" | null | "From Google Code: http://code.google.com/p/pandas/issues/detail?id=14
First of all, thanks very much for this very nice project. I find it extremely useful.
I ran into a very minor issue:
What steps will reproduce the problem?
1. Create a DataFrame with date objects in the index
2. Write to csv using toCSV method
3. Read DataFrame using parseCSV method
What is the expected output? What do you see instead?
I had hoped that the index of the parsed DataFrame would
consist of date objects, instead they are strings.
What version of the product are you using? On what operating system?
Trunk, revision 202. Ubuntu 10.10
Please provide any additional information below.
The reason is that toCSV writes the first column header as "index"
If it were to leave it blank, things work as I expect.
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} | [] | "closed" | false | null | [] | null | 2 | "2010-11-19T14:50:11" | "2010-12-17T02:45:20" | "2010-12-17T02:45:20" | "NONE" | null | "Using a stock debian lenny box with:
- numpy-1.3.0 built from source
- scipy 0.8.0 built from source
- matplotlib-1.0.0 built from source
- pandas-0.2beta (note: no problems with latest github pull)
# Errors: Install 0.2beta
mpenning@Bucksnort:~/src/pandas-0.2beta$ sudo python setup.py install
running install
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler options
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options
running build_src
building extension "pandas.lib.tseries" sources
pandas.lib - nothing done with h_files = ['pandas/lib/include/wirth.h']
building data_files sources
running build_py
creating build
creating build/lib.linux-i686-2.5
creating build/lib.linux-i686-2.5/pandas
copying pandas/**init**.py -> build/lib.linux-i686-2.5/pandas
copying pandas/info.py -> build/lib.linux-i686-2.5/pandas
copying pandas/version.py -> build/lib.linux-i686-2.5/pandas
copying pandas/setup.py -> build/lib.linux-i686-2.5/pandas
creating build/lib.linux-i686-2.5/pandas/core
copying pandas/core/mixins.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/**init**.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/series.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/collection.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/index.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/panel.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/pytools.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/matrix.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/daterange.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/frame.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/common.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/datetools.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/groupby.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/setup.py -> build/lib.linux-i686-2.5/pandas/core
copying pandas/core/api.py -> build/lib.linux-i686-2.5/pandas/core
creating build/lib.linux-i686-2.5/pandas/io
copying pandas/io/**init**.py -> build/lib.linux-i686-2.5/pandas/io
copying pandas/io/parsers.py -> build/lib.linux-i686-2.5/pandas/io
creating build/lib.linux-i686-2.5/pandas/lib
copying pandas/lib/**init**.py -> build/lib.linux-i686-2.5/pandas/lib
copying pandas/lib/build.py -> build/lib.linux-i686-2.5/pandas/lib
copying pandas/lib/bench.py -> build/lib.linux-i686-2.5/pandas/lib
copying pandas/lib/setup.py -> build/lib.linux-i686-2.5/pandas/lib
creating build/lib.linux-i686-2.5/pandas/sandbox
copying pandas/sandbox/**init**.py -> build/lib.linux-i686-2.5/pandas/sandbox
creating build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/plm.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/**init**.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/ols.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/math.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/interface.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/common.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/fama_macbeth.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/setup.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/var.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/moments.py -> build/lib.linux-i686-2.5/pandas/stats
copying pandas/stats/api.py -> build/lib.linux-i686-2.5/pandas/stats
creating build/lib.linux-i686-2.5/pandas/util
copying pandas/util/**init**.py -> build/lib.linux-i686-2.5/pandas/util
copying pandas/util/decorators.py -> build/lib.linux-i686-2.5/pandas/util
copying pandas/util/testing.py -> build/lib.linux-i686-2.5/pandas/util
running build_ext
customize UnixCCompiler
customize UnixCCompiler using build_ext
building 'pandas.lib.tseries' extension
compiling C sources
C compiler: gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC
creating build/temp.linux-i686-2.5
creating build/temp.linux-i686-2.5/pandas
creating build/temp.linux-i686-2.5/pandas/lib
creating build/temp.linux-i686-2.5/pandas/lib/src
compile options: '-I/usr/lib/python2.5/site-packages/numpy/core/include -Ipandas/lib/include/ -I/usr/lib/python2.5/site-packages/numpy/core/include -I/usr/include/python2.5 -c'
gcc: pandas/lib/src/wirth.c
gcc: pandas/lib/src/tseries.c
/usr/lib/python2.5/site-packages/numpy/core/include/numpy/__ufunc_api.h:183: warning: ?_import_umath? defined but not used
pandas/lib/src/tseries.c:1397: warning: ?__pyx_f_7tseries_get_int16_ptr? defined but not used
pandas/lib/src/tseries.c:1434: warning: ?__pyx_f_7tseries_get_int32_ptr? defined but not used
pandas/lib/src/tseries.c:1471: warning: ?__pyx_f_7tseries_get_int64_ptr? defined but not used
pandas/lib/src/tseries.c:1508: warning: ?__pyx_f_7tseries_get_double_ptr? defined but not used
gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions build/temp.linux-i686-2.5/pandas/lib/src/tseries.o build/temp.linux-i686-2.5/pandas/lib/src/wirth.o -o build/lib.linux-i686-2.5/pandas/lib/tseries.so
running scons
running install_lib
copying build/lib.linux-i686-2.5/pandas/lib/tseries.so -> /usr/lib/python2.5/site-packages/pandas/lib
running install_data
running install_egg_info
Removing /usr/lib/python2.5/site-packages/pandas-0.2beta.egg-info
Writing /usr/lib/python2.5/site-packages/pandas-0.2beta.egg-info
mpenning@Bucksnort:~/src/pandas-0.2beta$ cd
mpenning@Bucksnort:~/$
mpenning@Bucksnort:~$ python
Python 2.5.2 (r252:60911, Jan 24 2010, 14:53:14)
[GCC 4.3.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
> > > import pandas
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/usr/lib/python2.5/site-packages/pandas/**init**.py", line 14, in <module>
> > > from pandas.stats.api import *
> > > File "/usr/lib/python2.5/site-packages/pandas/stats/api.py", line 7, in <module>
> > > from pandas.stats.moments import *
> > > File "/usr/lib/python2.5/site-packages/pandas/stats/moments.py", line 365, in <module>
> > > _rolling_sum = _wrap_cython(tseries.roll_sum)
> > > AttributeError: 'module' object has no attribute 'roll_sum'
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} | [] | "closed" | false | null | [] | null | 13 | "2010-12-14T16:50:11" | "2010-12-19T05:55:29" | "2010-12-15T04:07:42" | "NONE" | null | "I've installed numpy from numpy-1.5.1-win32-superpack-python2.6.exe
and pandas from pandas-0.2.win32-py2.6.exe.
When I import \* from pandas I get the following warnings:
Warning (from warnings module):
File "C:\Program Files\Python26\lib\site-packages\pandas\core\index.py", line 7
from pandas.lib.tseries import map_indices
RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
Warning (from warnings module):
File "C:\Program Files\Python26\lib\site-packages\pandas\core\index.py", line 7
from pandas.lib.tseries import map_indices
RuntimeWarning: numpy.flatiter size changed, may indicate binary incompatibility
I've had no issues yet using pandas interactively in the window (idle), but if I have embedded the import in a module then I am not able to execute any of the code from the module.
Thoughts?
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} | [] | "closed" | false | null | [] | null | 4 | "2010-12-22T10:37:40" | "2011-01-01T23:16:26" | "2011-01-01T23:16:26" | "NONE" | null | "I'm not sure whether this is due to a problem with my local version of numpy or something else (in perhaps pandas?)... I did not see this previously (same numpy install and other dependencies) when I installed from Github trunk (sometime around November 15, 2010)...
```
[mpenning@Bucksnort ~]$ uname -a
Linux Bucksnort 2.6.26-2-686 #1 SMP Thu Sep 16 19:35:51 UTC 2010 i686 GNU/Linux
[mpenning@Bucksnort ~]$ cat /etc/issue
Debian GNU/Linux 5.0 \n \l
[mpenning@Bucksnort ~]$ sudo easy_install -U pandas
[sudo] password for mpenning:
Searching for pandas
Reading http://pypi.python.org/simple/pandas/
Reading http://pandas.sourceforge.net
Best match: pandas 0.3.0.beta
Downloading http://pypi.python.org/packages/source/p/pandas/pandas-0.3.0.beta.tar.gz#md5=18a39d6aa5df2f3515bada968554f049
Processing pandas-0.3.0.beta.tar.gz
Running pandas-0.3.0.beta/setup.py -q bdist_egg --dist-dir /tmp/easy_install-24C3ig/pandas-0.3.0.beta/egg-dist-tmp-Lc7eTD
warning: no files found matching 'LICENSE.txt'
warning: no files found matching 'README.txt'
/usr/lib/python2.5/site-packages/numpy/core/include/numpy/__ufunc_api.h:197: warning: ?_import_umath? defined but not used
pandas/lib/src/tseries.c:1535: warning: ?__pyx_f_7tseries_get_int16_ptr? defined but not used
pandas/lib/src/tseries.c:1572: warning: ?__pyx_f_7tseries_get_int32_ptr? defined but not used
pandas/lib/src/tseries.c:1609: warning: ?__pyx_f_7tseries_get_int64_ptr? defined but not used
pandas/lib/src/tseries.c:1646: warning: ?__pyx_f_7tseries_get_double_ptr? defined but not used
zip_safe flag not set; analyzing archive contents...
Adding pandas 0.3.0.beta to easy-install.pth file
Installed /usr/lib/python2.5/site-packages/pandas-0.3.0.beta-py2.5-linux-i686.egg
Processing dependencies for pandas
Finished processing dependencies for pandas
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/usr/lib/python2.5/atexit.py", line 24, in _run_exitfuncs
func(*targs, **kargs)
File "/usr/lib/python2.5/site-packages/numpy/distutils/misc_util.py", line 251, in clean_up_temporary_directory
from numpy.distutils import log
SystemError: Parent module 'numpy.distutils' not loaded
Error in sys.exitfunc:
Traceback (most recent call last):
File "/usr/lib/python2.5/atexit.py", line 24, in _run_exitfuncs
func(*targs, **kargs)
File "/usr/lib/python2.5/site-packages/numpy/distutils/misc_util.py", line 251, in clean_up_temporary_directory
from numpy.distutils import log
SystemError: Parent module 'numpy.distutils' not loaded
[mpenning@Bucksnort ~]$ python
Python 2.5.2 (r252:60911, Jan 24 2010, 14:53:14)
[GCC 4.3.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> quit()
[mpenning@Bucksnort ~]$
```
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} | [] | "closed" | false | null | [] | null | 3 | "2010-12-24T07:27:52" | "2010-12-24T21:29:45" | "2010-12-24T16:27:12" | "NONE" | null | "pypi install of pandas still isn't working on 2.7, even with all dependencies
Python 2.7.1 (r271:86832, Nov 27 2010, 18:30:46) [MSC v.1500 32 bit (Intel)] on
win32
Type "help", "copyright", "credits" or "license" for more information.
> > > import pandas
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "pandas__init__.py", line 12, in <module>
> > > from pandas.core.api import *
> > > File "pandas\core\api.py", line 8, in <module>
> > > from pandas.core.common import isnull, notnull
> > > File "pandas\core\common.py", line 6, in <module>
> > > import pandas.lib.tseries as tseries
> > > ImportError: No module named tseries
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} | [] | "closed" | false | null | [] | null | 2 | "2010-12-24T07:32:39" | "2010-12-24T21:28:59" | "2010-12-24T21:28:59" | "NONE" | null | "Old bug opened on google code showed that workaround for the pypi install failures should be to download latest source and compile as such:
python setup.py build --compiler=mingw32
python setup.py install
tseries.c has two errors stopping compilation
pandas\lib\src\tseries.c: In function '__Pyx_c_absf':
pandas\lib\src\tseries.c:18906:25: error: #if with no expression
pandas\lib\src\tseries.c: In function '__Pyx_c_abs':
pandas\lib\src\tseries.c:19026:25: error: #if with no expression
pandas\lib\src\tseries.c: At top level:
should that be #ifdef?
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} | [] | "closed" | false | null | [] | null | 1 | "2011-01-03T17:15:19" | "2011-01-03T23:23:47" | "2011-01-03T23:23:47" | "NONE" | null | "Using python 2.5.2, pandas 0.3 and numpy 1.5.1 on a debian lenny box... mean() works, but not median()
```
>>> df1['b'].truncate(before=dt.date(2010,12,2),after=dt.date(2010,12,4))
2010-12-02 6
2010-12-03 7
2010-12-04 8
>>> df1['b'].truncate(before=dt.date(2010,12,2),after=dt.date(2010,12,4)).median()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "build/bdist.linux-i686/egg/pandas/core/series.py", line 486, in median
File "moments.pyx", line 70, in tseries.median (pandas/lib/src/tseries.c:5967)
File "moments.pyx", line 28, in tseries.kth_smallest (pandas/lib/src/tseries.c:5539)
ValueError: Buffer dtype mismatch, expected 'double_t' but got 'long'
>>> df1['b'].truncate(before=dt.date(2010,12,2),after=dt.date(2010,12,4)).mean()
7.0
```
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} | [] | "closed" | false | null | [] | null | 1 | "2011-01-03T23:29:47" | "2011-02-20T00:53:44" | "2011-02-20T00:53:44" | "CONTRIBUTOR" | null | "Small bug in the FamaMacBeth class at lines 105-106-- the printed form of the regression results gives the STD twice, rather than the t-stat:
<code>
std_beta = self._results['std_beta'][i]
t_stat = self._results['std_beta'][i]
</code>
Last one should be t_stat instead of std_beta the second time.
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"https://api.github.com/repos/pandas-dev/pandas/issues/28" | "https://api.github.com/repos/pandas-dev/pandas" | "https://api.github.com/repos/pandas-dev/pandas/issues/28/labels{/name}" | "https://api.github.com/repos/pandas-dev/pandas/issues/28/comments" | "https://api.github.com/repos/pandas-dev/pandas/issues/28/events" | "https://github.com/pandas-dev/pandas/issues/28" | 518,963 | "MDU6SXNzdWU1MTg5NjM=" | 28 | "what is the easiest way to plot a timeseries and dataframe?" | {
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} | [] | "closed" | false | null | [] | null | 2 | "2011-01-11T04:14:15" | "2011-01-29T19:13:49" | "2011-01-29T19:13:49" | "NONE" | null | "it looks like ts.plot and df.plot is not implemented yet. I watched Wes's PyCon 2010 video where he use some functions fplot (presumably it is defined in the pycon_demo.py that was run at the beginning of the demo). pandas is a great package. Hope it will be kept active and keep expanding. Great work. Thanks.
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} | [] | "closed" | false | null | [] | null | 2 | "2011-01-11T12:15:39" | "2011-06-23T04:47:01" | "2011-06-23T04:47:01" | "NONE" | null | "```
>>> # dataframe is indexed at a second granularity, using eastern time
>>> dataframe <class 'pandas.core.frame.DataFrame'>
Index: 24676 entries , 2010-10-04 00:03:49-04:00 to 2010-10-04 23:59:55-04:00
etf 10897 non-null values
etfvol 10897 non-null values
fut 17988 non-null values
futvol 17988 non-null values
ticx 7880 non-null values
vix 7465 non-null values
vixvol 7465 non-null values
>>> dataframe['etf'].index[0]
datetime.datetime(2010, 10, 4, 0, 3, 49, tzinfo=<DstTzInfo 'US/Eastern' EDT-1 day, 20:00:00 DST>)
>>>
>>> # et is a pytz timezone object for eastern time...
>>> et
<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>
>>> dataframe['fut'].asfreq(et.localize(pn.DateOffset(seconds=1)), method='pad')
Traceback (most recent call last):
File "<console>", line 1, in <module>
File "/usr/lib/python2.5/site-packages/pytz-2010o-py2.5.egg/pytz/tzinfo.py", line 262, in localize
if dt.tzinfo is not None:
AttributeError: 'DateOffset' object has no attribute 'tzinfo'
>>>
>>> dataframe['fut'].asfreq(pn.DateOffset(seconds=1), method='pad') Traceback (most recent call last):
File "<console>", line 1, in <module>
File "/usr/lib/python2.5/site-packages/pandas/core/series.py", line 1103, in asfreq
dateRange = DateRange(self.index[0], self.index[-1], offset=freq)
File "/usr/lib/python2.5/site-packages/pandas/core/daterange.py", line 70, in __new__
fromInside = start is not None and start > _CACHE_START
TypeError: can't compare offset-naive and offset-aware datetimes
>>>
```
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} | [] | "closed" | false | null | [] | null | 3 | "2011-01-11T12:31:34" | "2011-06-14T14:17:06" | "2011-06-14T14:17:06" | "NONE" | null | "```
>>> df2['a']
2010-12-01 00:00:00 1
2010-12-02 00:00:00 2
2010-12-03 00:00:00 3
2010-12-04 00:00:00 4
>>> df2['a'].asfreq(pn.DateOffset(seconds=1),method='pad')
## NOTE: The above one-liner runs for over 20 minutes on a 2Ghz Xeon, python 2.5.2, numpy 1.5.1,
## & pandas head from 2011-01-05. It also consumed > 15% (500MB!!!) of available DRAM
## before I manually killed it
[mpenning@Bucksnort tickdata]$ free
total used free shared buffers cached
Mem: 3894944 3771540 123404 0 192340 2328984
-/+ buffers/cache: 1250216 2644728
Swap: 2830328 596 2829732
[mpenning@Bucksnort tickdata]$ cat /proc/cpuinfo
...
processor : 3
vendor_id : GenuineIntel
cpu family : 15
model : 2
model name : Intel(R) Xeon(TM) CPU 2.00GHz
stepping : 7
cpu MHz : 1995.840
cache size : 512 KB
physical id : 3
siblings : 2
core id : 0
cpu cores : 1
apicid : 7
initial apicid : 7
fdiv_bug : no
hlt_bug : no
f00f_bug : no
coma_bug : no
fpu : yes
fpu_exception : yes
cpuid level : 2
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe pebs bts cid xtpr
bogomips : 3991.51
clflush size : 64
power management:
[mpenning@Bucksnort tickdata]$
```
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} | [] | "closed" | false | null | [] | null | 3 | "2011-01-13T04:05:11" | "2011-02-20T00:07:53" | "2011-02-20T00:07:53" | "NONE" | null | "This may not be the best place to post this question b/c it is not an "issue", just a question that I don't know where else I can post it to. I am just wondering whether there is a single pdf help or documentation file, instead of html help pages on the sourceforge website. Also, I am wondering whether we have a faq or forum that people discuss about usage/tutorial/success stories etc on using pandas, other than this one? may be pystatmodel? Thanks again, Wes for creating this package! -alex
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"https://api.github.com/repos/pandas-dev/pandas/issues/32" | "https://api.github.com/repos/pandas-dev/pandas" | "https://api.github.com/repos/pandas-dev/pandas/issues/32/labels{/name}" | "https://api.github.com/repos/pandas-dev/pandas/issues/32/comments" | "https://api.github.com/repos/pandas-dev/pandas/issues/32/events" | "https://github.com/pandas-dev/pandas/issues/32" | 535,032 | "MDU6SXNzdWU1MzUwMzI=" | 32 | "Does pandas support piecewise/multi-period regression?" | {
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} | [] | "closed" | false | null | [] | null | 3 | "2011-01-18T20:28:41" | "2011-01-20T17:42:12" | "2011-01-20T17:42:12" | "NONE" | null | "Hi,
I need to do piecewise or multi-period regression where the breakpoint is determined automatically by the algorithm itself. Does pandas support that? Thanks a lot.
-alex
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} | [] | "closed" | false | null | [] | null | 1 | "2011-01-22T20:36:36" | "2011-02-20T00:50:55" | "2011-02-20T00:50:55" | "MEMBER" | null | "There are a few potential NaN-casting problems floating around the codebase, e.g.:
```
df1 = pandas.DataFrame({'x':[5]})
df2 = pandas.DataFrame({'x':[1]})
df1.combineAdd(df2)
```
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"https://api.github.com/repos/pandas-dev/pandas/issues/34" | "https://api.github.com/repos/pandas-dev/pandas" | "https://api.github.com/repos/pandas-dev/pandas/issues/34/labels{/name}" | "https://api.github.com/repos/pandas-dev/pandas/issues/34/comments" | "https://api.github.com/repos/pandas-dev/pandas/issues/34/events" | "https://github.com/pandas-dev/pandas/issues/34" | 547,967 | "MDU6SXNzdWU1NDc5Njc=" | 34 | "reindex(method="backfill") and periods within the last date" | {
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} | [] | "closed" | false | null | [] | null | 2 | "2011-01-24T20:15:05" | "2011-02-20T00:52:36" | "2011-02-20T00:52:36" | "NONE" | null | "I have a DataFrame with 1 minute timestamps, pw.
I have a monthly average of the values:
ma_pw=pw.groupby(BMonthEnd(+1).rollforward).aggregate(np.mean)
When I do a reindex to push that average back into all of the periods for the month,
the last day's values are NaN. Here's my reindex attempt:
reindexed = ma_pw.reindex(pw.index, method="backfill").
I have worked around this by forcing the hour in my monthly data to something past the last minute that I care about in my by-minute data as follows, but this seems incredibly crude:
from dateutil.relativedelta import *
newi = [x + relativedelta(hour=20) for x in ma_pw.index]
ma_pw.index = newi[:]
Then my original reindex works.
Is there a clean way to instruct the reindex method to include the datetime objects having the same datetime.date on that last day of the month?
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"https://api.github.com/repos/pandas-dev/pandas/issues/35" | "https://api.github.com/repos/pandas-dev/pandas" | "https://api.github.com/repos/pandas-dev/pandas/issues/35/labels{/name}" | "https://api.github.com/repos/pandas-dev/pandas/issues/35/comments" | "https://api.github.com/repos/pandas-dev/pandas/issues/35/events" | "https://github.com/pandas-dev/pandas/pull/35" | 617,056 | "MDExOlB1bGxSZXF1ZXN0NzgyMTc=" | 35 | "Added hist method and added auto_x argument to plot method." | {
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} | [] | "closed" | false | null | [] | null | 2 | "2011-02-22T08:07:35" | "2011-02-27T05:17:56" | "2011-02-25T01:41:56" | "NONE" | null | "I have added hist method to frame.py and series.py, and added auto_x argument to plot method, which can be useful for the cases where the index does not consist of numeric values.
-Joon
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} | [] | "closed" | false | null | [] | null | 5 | "2011-02-25T19:07:03" | "2013-09-17T12:20:45" | "2011-03-02T12:24:08" | "NONE" | null | "I'm on a new computer with Windows 7.
I've installed the 32 bit versions of python-2.7.1 and all subsequent requirements.
pandas-0.3.0.win32-py2.7.exe does not complete. It just stops and windows claims it is not responding. The same phenomenon happened installing tables-2.2.1.win32-py2.7.exe. I'm assuming that pytables is recommended but not required for pandas. Anyway, both of these installs do not work.
Suggestions?
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When I play around with a dataset I use this describe thing a lot so I thought it would be convenient to have this with DataFrame.
I think this is crudely implemented so if you think there is a better way please let me know!
-Joon
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I think it would be great if DataFrame is able to store additional property (list of strings) which contains the description for each variable (column). And then it can be shown by themselves with variable names as well as in the output of pandas.DataFrame.info(). I think with this DataFrame will be pretty much self contained. Right now I need to keep additional object or a text file which contains those descriptions.
Please let me know what do you think!
-Joon
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def create_panels_join(a, b):
""" return a join of the 2 panels """
d = dict([ (i,(a[i].T.join(v.T)).T) for i, v in b.iteritems() ])
return p.WidePanel.fromDict(d)
# this is a 'more' correct version, but have to assure that a[i] is not None
def create_panels_join(a, b):
""" return a join of the 2 panels """
items = set(a.items) + set(b.items)
WidePanel.fromDict(dict([ (i,(a[i].T.join(b[i].T)).T) for i in items ]))
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I am very new to github and miss a discussion board for pandas. Not sure if issues is the right place for my question, so feel free to delete it, but please point to the right place for questions.
The question: I need to aggregate hourly time series to the daily scale, that would include all days, 7 per week. Currently i find only bday class in datetools:
<pre>
daily_ts = ts.groupby(lambda x: datetools.bday(x)).aggregate(np.nansum)
</pre>
But bday will skip weekends. datetools.day seem to return timeseries with the same time resolution.
How to include weekend days?
Many thanks for developing pandas, it's truly nice, and really fast, even when working with large datasets!
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ValueError: timestamp out of range for platform localtime()/gmtime() function
File "C:\dev\MktDB\test_continuation.py", line 59, in <module>
main()
File "C:\Python27\lib\site-packages\pandas-0.3.0-py2.7-win32.egg\pandas\io\pytables.py", line 157, in _read_group
File "C:\Python27\lib\site-packages\pandas-0.3.0-py2.7-win32.egg\pandas\io\pytables.py", line 173, in _read_frame
File "C:\Python27\lib\site-packages\pandas-0.3.0-py2.7-win32.egg\pandas\io\pytables.py", line 210, in _read_index
File "C:\Python27\lib\site-packages\pandas-0.3.0-py2.7-win32.egg\pandas\io\pytables.py", line 227, in _unconvert_index
File "C:\Users\Shon\AppData\Roaming\Python-Eggs\pandas-0.3.0-py2.7-win32.egg-tmp\pandas\lib\tseries.pyd", line 45, in tseries.array_to_datetime (pandas\lib\src\tseries.c:14378)
File "C:\Users\Shon\AppData\Roaming\Python-Eggs\pandas-0.3.0-py2.7-win32.egg-tmp\pandas\lib\tseries.pyd", line 20, in tseries.to_datetime (pandas\lib\src\tseries.c:13910)
Any guidance would be most appreciated.
Shon
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} | 1 | "2011-06-03T10:50:07" | "2012-07-12T16:51:03" | "2012-07-12T16:51:03" | "MEMBER" | null | "Currently using skiplist, unnecessary
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```
filledRatio = ibm / ibmMonthly.reindex(ibm.index, fillMethod='pad')
```
should be:
```
filledRatio = ibm / ibmMonthly.reindex(ibm.index, method='pad')
```
As reindex is defined in core/series.py as
```
def reindex(self, index=None, method=None):
```
This error is repeated in the documentation at http://pandas.sourceforge.net (reindex uses the argument fillMethod instead of method).
running regressions.py, I get the following error:
Traceback (most recent call last):
File "regressions.py", line 30, in <module>
model = ols(y=Y, x=X)
File "/usr/local/lib/python2.6/dist-packages/pandas/stats/interface.py", line 117, in ols
return klass(**kwargs)
File "/usr/local/lib/python2.6/dist-packages/pandas/stats/ols.py", line 56, in **init**
self.sm_ols = sm.OLS(self._y_raw, self._x.values).fit()
AttributeError: 'module' object has no attribute 'OLS'
It's possible I'm somehow combining incompatible versions of pandas, but I don't see how, considering I downloaded pandas today, once. The first bug, at least seems like a simple code error.
Hopefully this is of some help,
Colum
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] | "closed" | false | null | [] | null | 1 | "2011-06-21T15:49:54" | "2011-06-23T04:16:33" | "2011-06-23T04:16:33" | "MEMBER" | null | "Something like
```
df.ix[0, ['C', 'B', A']]
```
is currently performing a wasteful reindex(columns=['C', 'B', A']), need to have some better way to infer the "right" order to perform the operations in
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```
x = Series([NaN,1.,NaN,3.,NaN],['a','b','c','d','e'], dtype=object)
x.fillna(method='pad')
```
Not OK
```
x = Series([NaN,1.,NaN,3.,NaN],['z','a','b','c','d'], dtype=object)
x.fillna(method='pad')
```
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} | [] | "closed" | false | null | [] | null | 1 | "2011-06-29T00:59:24" | "2011-06-29T02:07:07" | "2011-06-29T02:06:48" | "CONTRIBUTOR" | null | "Hi Wes, i've been following your pandas work for a while, and regularly install it on my ubuntu based systems. I generally use the stdeb (https://github.com/astraw/stdeb) package for creating .deb files from python packages. Unfortunately, some of your recent changes in setup.py in pandas caused a problem with building using stdeb. Essentially the problem is that debhelper tries to run "python setup.py clean -a" at some point, which doesn't work because the customized clean command does not have a "-a" option. I just added a dummy argument "--all, -a" argument that has no effect.
No worries if you'd rather not merge it, just wanted to check.
thanks
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```
In [12]: arr = Series(np.random.randn(10), index=np.arange(10), dtype=object)
In [13]: arr
Out[13]:
0 -0.475934738891
1 0.602096986911
2 0.731158853064
3 -0.268872970066
4 0.607786684074
5 -0.0200444918205
6 1.93289485517
7 1.14860246486
8 -0.138246858663
9 0.382736317471
In [14]: 1. / arr
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/Users/wesm/Downloads/<ipython-input-14-6df87fcc597b> in <module>()
----> 1 1. / arr
TypeError: unsupported operand type(s) for /: 'float' and 'Series'
```
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ser.fillna(method='backfill')
GIVES
ValueError: unknown dtype code in numpy.pxd (0)
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} | [] | "closed" | false | null | [] | null | 3 | "2011-07-15T04:51:09" | "2011-07-15T06:54:06" | "2011-07-15T06:54:06" | "NONE" | null | "sudo python setup.py -v build # fails on Mac OS 10.6 or something
can't convert the .pyx files to .c
hard linking pandas/src/skiplist.pyx -> pandas-0.4.0.dev20110715_027c90b/pandas/src
'pandas/src/sparse.c' not a regular file -- skipping
hard linking pandas/src/sparse.pyx -> pandas-0.4.0.dev20110715_027c90b/pandas/src
'pandas/src/tseries.c' not a regular file -- skipping
changing the setup.py as follows:
@line10
- from setuptools import setup
- from scipy_distutils.core import setup
makes it work.
thx Pearu: http://osdir.com/ml/python.f2py.user/2003-11/msg00008.html
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} | [] | "closed" | false | null | [] | null | 3 | "2011-07-15T21:18:40" | "2011-07-15T23:44:55" | "2011-07-15T23:44:55" | "CONTRIBUTOR" | null | "See attached patch.
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} | 20 | "2011-07-15T23:11:41" | "2014-07-30T01:12:20" | "2014-07-30T01:12:20" | "NONE" | null | "Hi,
Currently, sub-classing pandas objects is not as easy as it could be.
This is the result of many methods explicitly creating specific classes. E.g. the '_combine_const' method from 'matrix' explicitly returns a DataMatrix object:
def _combine_const(self, other, func):
if not self:
return self
```
# TODO: deal with objects
return DataMatrix(func(self.values, other), index=self.index,
columns=self.columns)
```
Therefore, arithmetic operations (and some other operations) performed on a new class MyDataMatrix, inheriting form DataMatrix, return a DataMatrix object, rather than a MyDataMatrix object.
I can get around the problem using the ugly hack of overriding the problem methods, and forcing the output to the new class. E.g.,
def _combine_const(self, other, func):
temp = super(MyDataMatrix, self)._combine_const(other, func)
temp.__class__ = self.**class**
return temp
However, I feel it would be easier to just change the original methods to return the class of the calling object, rather than a fixed class. E.g.,
def _combine_const(self, other, func):
if not self:
return self
```
# TODO: deal with objects
return self.__class__(func(self.values, other), index=self.index,
columns=self.columns)
```
Hope this makes sense, and I'm not missing something.
Thanks for a very useful package!
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```
dm = DataFrame({'col1':[1,2],'col2':[1,2]})
dm[dm!=0] = 5
```
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```
mean(arr, na.rm=T)
```
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} | 0 | "2011-07-18T15:43:35" | "2011-11-10T04:08:23" | "2011-11-10T04:08:23" | "MEMBER" | null | "To enable easy matrix multiplication with DataFrame:
```
df.dot(series)
```
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